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Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.

In 2011–2012 Russia experienced a wave of mass protests surrounding the Duma and presidential elections. The protests, however, faded shortly after the second election. We study the Russian political discourse on Twitter during this period and the main actors involved: the pro-government camp, the opposition, and the general public. We analyze around 700,000 Twitter messages and investigate the social networks of the most active Twitter users. Our analysis shows that pro-government users employed a variety of communication strategies to shift the political discourse and marginalize oppositional voices on Twitter. This demonstrates how authorities can disempower regime critics and successfully manipulate public opinion on social media.

Intro -- Preface -- Organization -- Contents -- Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets -- 1 Introduction -- 2 Checkworthiness -- 2.1 General Definition -- 2.2 Datasets and Computational Models -- 2.3 Fact-Check Organisations -- 2.4 Empirical Analysis of Checkworthiness -- 3 Scope and Language Use -- 4 Imbalance and Noise -- 5 Conclusion -- References -- How Fake News Affect Trust in the Output of a Machine Learning System for News Curation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Rating News Stories (Phase 1) -- 3.2 Rating News Recommendations (Phase 2) -- 3.3 Types of News Recommendations -- 4 Results -- 4.1 Trust Ratings for Algorithmic News Curation (RQ1) -- 4.2 Trustworthy News Recommendations (RQ2) -- 4.3 Fake News Stories (RQ3) -- 5 Discussion -- 6 Limitations -- 7 Conclusion -- References -- A Dip into a Deep Well: Online Political Advertisements, Valence, and European Electoral Campaigning -- 1 Introduction -- 2 Hypotheses and Related Work -- 3 Materials and Methods -- 3.1 Data -- 3.2 Methods -- 4 Results -- 5 Conclusion -- References -- Misinformation from Web-based News Media? Computational Analysis of Metabolic Disease Burden for Chinese -- 1 Introduction -- 1.1 Misinformation Analysis -- 2 Methodology -- 2.1 Sampling -- 2.2 Computational Taxonomy -- 3 Results -- 4 Discussion -- 4.1 Limitations -- 5 Conclusion -- References -- Students Assessing Digital News and Misinformation -- 1 Introduction -- 2 Previous Research -- 3 Material and Method -- 4 Results -- 4.1 Aftonbladet - Distinguishing News from Advertising -- 4.2 Smoking - Evaluating Evidence -- 4.3 New Legislation Against Hate Crimes - Comparing News Without Source Information -- 4.4 Weight Loss - Comparing Complex Articles on Health -- 5 Conclusions -- 6 Concluding Discussion -- 7 Limitations.

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values.

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach. ; Published (Publication status)

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values. ; Published (Publication status)